Coarse-to-Fine Approach for Urban Area Interpretation Using TerraSAR-X Data

Chaabouni-Chouayakh, Houda; Datcu, Mihai

doi:10.1109/lgrs.2009.2020070

Cited by 22 publications

(8 citation statements)

References 13 publications

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“…Numerous studies concerning automated land cover/land use classifications of specific datasets in urban areas, or at least in partly built-up areas, can be found in the literature (see, for example, [1][2][3][4][5] for aerial image data, [6][7][8][9] for laser scanner data, [10][11][12][13][14][15] for high-resolution optical satellite images, and [16][17][18][19][20] for high-resolution SAR images). Some details of selected studies are presented in Table 1 (we selected studies that used only remotely sensed data for classification, presented the overall accuracy of the classification, and had classes most similar to our study).…”

Section: Comparison Of New Remotely Sensed Datasets For Land Cover CLmentioning

confidence: 99%

Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points

Matikainen

Karila

2011

Remote Sensing

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Abstract:In order to better understand and exploit the rich information content of new remotely sensed datasets, there is a need for comparative land cover classification studies. In this study, the automatic classification of a suburban area was investigated by using (1) digital aerial image data; (2) digital aerial image data and laser scanner data; (3) a high-resolution optical QuickBird satellite image; (4) high-resolution airborne synthetic aperture radar (SAR) data; and (5) SAR data and laser scanner data. A segment-based approach was applied. The classification rules for distinguishing buildings, trees, vegetated ground, and non-vegetated ground were created automatically by using permanent test field points in a training area and the classification tree method. The accuracy of the results was evaluated by using test field points in validation areas. The highest overall accuracies were obtained when laser scanner data were used to separate high and low objects: 97% in Test 2, and 82% in Test 5. The overall accuracies in the other tests were 74% (Test 1), 67% (Test 3), and 68% (Test 4). An important contributing factor for the lower accuracy in Tests 3 and 4 was the lower spatial resolution of the datasets. The classification tree method and test field points provided a feasible and automated means of comparing the classifications. The approach is well suited for rapid analyses of new datasets to predict their quality and potential for land cover classification.

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Section: Comparison Of New Remotely Sensed Datasets For Land Cover CLmentioning

confidence: 99%

Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points

Matikainen

Karila

2011

Remote Sensing

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“…Marin et al [19] introduced a new approach to building change detection in multitemporal VHR SAR images based on backscattering variability. A semiautomatic segmentation-based tool for urban area interpretation in SAR images was proposed in [20]. Voisin et al [21] presented a new model combining amplitude SAR data and textural information into a Markov random field model to address the problem of classifying images of urban areas.…”

Section: Resultsmentioning

confidence: 99%

Urban Areas Enhancement in Multitemporal SAR RGB Images Using Adaptive Coherence Window and Texture Information

Amitrano

Belfiore

Cecinati

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we present a technique for improving the representation of built-up features in model-based multitemporal synthetic aperture radar (SAR) RGB composites. The proposed technique exploits the multitemporal adaptive processing (MAP3) framework to generate an a priori information which is used to implement an adaptive selection of the coherence window size. Image texture is used to support the coherence information in case of decorrelation. The coherence information, powered by texture analysis, and combined with backscattering amplitude, provides a unique representation of built-up features. This allows for an immediate detection of urban agglomerates by human operators, and is an advantaged starting point for urban area extraction algorithms.Index Terms-Data representation, fuzzy logic, multitemporal synthetic aperture radar (SAR), urban areas.

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“…To compare the performance of the algorithm detailed in this paper to those of others, four alternatives were identified. The algorithms used for the comparison were the mean-shift algorithm [42] implemented within the Orfeo toolbox [47], as it is widely cited (e.g., [48][49][50]) as an approach that produced good results on a wide variety of Earth Observation (EO) data, the eCognition multi-resolution segmentation algorithm [28], as the algorithm most commonly used within the literature (e.g., [1,2,51]), and the Quickshift algorithm of Vedaldi and Soatto [52] and the algorithm of Felzenszwalb and Huttenlocher [53], implemented within the scikit-image library and interfaced within the RSGISLib library [54], as examples of more recent approaches from the computer vision community applicable to EO data. A SPOT-5 scene (a subset of which is shown in Figure 10A), which represents a range of land covers and uses, was used for the experiment.…”

Section: Comparison To Other Algorithmsmentioning

confidence: 99%

Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

2019

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Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib).

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Coarse-to-Fine Approach for Urban Area Interpretation Using TerraSAR-X Data

Cited by 22 publications

References 13 publications

Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points

Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points

Urban Areas Enhancement in Multitemporal SAR RGB Images Using Adaptive Coherence Window and Texture Information

Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

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